arXiv subjects

I. Stergakis

Publications and source records attributed to I. Stergakis.

2 recordsLinked to original sources

arXiv2026

Classification of Compact Stars via Machine Learning and Neural Network Models

Recent advances in multimessenger astronomy, particularly through gravitational-wave observations of compact-object mergers, have significantly improved our understanding of dense matter. Nevertheless, the internal composition of compact stars remains uncertain. Depending on the underlying equation of state (EoS), these objects may be neutron stars composed primarily of nucleons, quark stars made of deconfined quark matter, or hybrid stars containing both hadronic and quark phases. More exotic constituents, such as hyperons, meson condensates, or dark matter, have also been proposed. In this work, we investigate whether the internal composition of compact stars can be inferred from observable quantities, including mass, radius, and tidal deformability. To address this problem, we employ machine-learning and deep-learning techniques trained on a larg dataset of EoSs describing both neutron stars and quark stars. From these EoSs, we generate the corresponding mass radius relations spanning a wide range of stellar configurations. The resulting dataset is used to train and evaluate classification models aimed at identifying the nature of compact objects from their macroscopic properties. Our results indicate that suitable combinations of observables can distinguish neutron stars from quark stars with very high accuracy. These findings demonstrate the potential of machine-learning approaches as tools for probing the composition of dense matter. However, further studies incorporating additional scenarios, including hybrid stars and other exotic forms of matter, are required to establish the robustness and general applicability of this methodology.

astro-ph.HE↗

arXiv2025

Machine and Deep Learning Regression for Compact Object Equations of State

A central open problem in nuclear physics is the determination of a physically robust equation of state (EoS) for dense nuclear matter, which directly informs our understanding of the internal composition and macroscopic properties of compact objects such as neutron stars and quark stars. Traditional efforts have relied primarily on theoretical modeling grounded in nuclear and particle physics, with subsequent validation against empirical constraints from heavy ion collisions and, increasingly, multimessenger astrophysical observations. Recent developments, however, have introduced complementary analytical strategies that merge theoretical modeling with advanced data driven methodologies. In particular, Bayesian inference, machine learning, and deep learning have emerged as powerful tools for constraining the EoS and extracting physical insight from complex observational datasets. In this work, we employ state of the art machine learning and deep learning techniques to analyze mass radius relations of compact objects with the aim of reconstructing or inferring their underlying equations of state. The analysis is based on an extensive library of physically consistent, multimodal EoSs for neutron stars and a corresponding set for quark stars, each constructed to satisfy established theoretical and observational constraints. By leveraging the predictive capacity of these computational frameworks, we demonstrate the potential of data-driven approaches to provide refined insights into the behavior of matter at supranuclear densities and to contribute to a more unified understanding of the dense matter EoS.

nucl-th↗